Manuel Egele

A survey on automated dynamic malware-analysis techniques and tools

Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.

Defending Browsers against Drive-by Downloads: Mitigating Heap-Spraying Code Injection Attacks

Drive-by download attacks are among the most common methods for spreading malware today. These attacks typically exploit memory corruption vulnerabilities in web browsers and browser plug-ins to execute shellcode, and in consequence, gain control of a victims computer. Compromised machines are then used to carry out various malicious activities, such as joining botnets, sending spam emails, or participating in distributed denial of service attacks. To counter drive-by downloads, we propose a technique that relies on x86 instruction emulation to identify JavaScript string buffers that contain shellcode. Our detection is integrated into the browser, and performed before control is transfered to the shellcode, thus, effectively thwarting the attack. The solution maintains fair performance by avoiding unnecessary invocations of the emulator, while ensuring that every buffer with potential shellcode is checked. We have implemented a prototype of our system, and evaluated it over thousands of malicious and legitimate web sites. Our results demonstrate that the system performs accurate detection with no false positives.

An empirical study of cryptographic misuse in android applications

Developers use cryptographic APIs in Android with the intent of securing data such as passwords and personal information on mobile devices. In this paper, we ask whether developers use the cryptographic APIs in a fashion that provides typical cryptographic notions of security, e.g., IND-CPA security. We develop program analysis techniques to automatically check programs on the Google Play marketplace, and find that 10.327 out of 11,748 applications that use cryptographic APIs -- 88% overall -- make at least one mistake. These numbers show that applications do not use cryptographic APIs in a fashion that maximizes overall security. We then suggest specific remediations based on our analysis towards improving overall cryptographic security in Android applications.

A solution for the automated detection of clickjacking attacks

Clickjacking is a web-based attack that has recently received a wide media coverage. In a clickjacking attack, a malicious page is constructed such that it tricks victims into clicking on an element of a different page that is only barely (or not at all) visible. By stealing the victims clicks, an attacker could force the user to perform an unintended action that is advantageous for the attacker (e.g., initiate an online money transaction). Although clickjacking has been the subject of many discussions and alarming reports, it is currently unclear to what extent clickjacking is being used by attackers in the wild, and how significant the attack is for the security of Internet users. In this paper, we propose a novel solution for the automated and efficient detection of clickjacking attacks. We describe the system that we designed, implemented and deployed to analyze over a million unique web pages. The experiments show that our approach is feasible in practice. Also, the empirical study that we conducted on a large number of popular websites suggests that clickjacking has not yet been largely adopted by attackers on the Internet.

Removing web spam links from search engine results

Web spam denotes the manipulation of web pages with the sole intent to raise their position in search engine rankings. Since a better position in the rankings directly and positively affects the number of visits to a site, attackers use different techniques to boost their pages to higher ranks. In the best case, web spam pages are a nuisance that provide undeserved advertisement revenues to the page owners. In the worst case, these pages pose a threat to Internet users by hosting malicious content and launching drive-by attacks against unsuspecting victims. When successful, these drive-by attacks then install malware on the victims’ machines. In this paper, we introduce an approach to detect web spam pages in the list of results that are returned by a search engine. In a first step, we determine the importance of different page features to the ranking in search engine results. Based on this information, we develop a classification technique that uses important features to successfully distinguish spam sites from legitimate entries. By removing spam sites from the results, more slots are available to links that point to pages with useful content. Additionally, and more importantly, the threat posed by malicious web sites can be mitigated, reducing the risk for users to get infected by malicious code that spreads via drive-by attacks.

Challenges for dynamic analysis of iOS applications

Recent research indicates that mobile platforms, such as Android and Apples iOS increasingly face the threat of malware. These threats range from spyware that steals privacy sensitive information, such as location data or address book contents to malware that tries to collect ransom from users by locking the device and therefore rendering the device useless. Therefore, powerful analysis techniques and tools are necessary to quickly provide an analyst with the necessary information about an application to assess whether this application contains potentially malicious functionality. In this work, we focus on the challenges and open problems that have to be overcome to create dynamic analysis solutions for iOS applications. Additionally, we present two proof-of-concept implementations tackling two of these challenges. First, we present a basic dynamic analysis approach for iOS applications demonstrating the feasibility of dynamic analysis on iOS. Second, addressing the challenge that iOS applications are almost always user interface driven, we also present an approach to automatically exercise an applications user interface. The necessity of exercising application user interfaces is demonstrated by the difference in code coverage that we achieve with (60%) and without (16%) such techniques. Therefore, this work is a first step towards comprehensive dynamic analysis for iOS applications.

Mitigating Drive-By Download Attacks: Challenges and Open Problems

Malicious web sites perform drive-by download attacks to infect their visitors with malware. Current protection approaches rely on black- or white-listing techniques that are difficult to keep up-to-date. As todays drive-by attacks already employ encryption to evade network level detection we propose a series of techniques that can be implemented in web browsers to protect the user from such threats. In addition, we discuss challenges and open problems that these mechanisms face in order to be effective and efficient.

Hit 'em where it hurts: a live security exercise on cyber situational awareness

Live security exercises are a powerful educational tool to motivate students to excel and foster research and development of novel security solutions. Our insight is to design a live security exercise to provide interesting datasets in a specific area of security research. In this paper we validated this insight, and we present the design of a novel kind of live security competition centered on the concept of Cyber Situational Awareness. The competition was carried out in December 2010, and involved 72 teams (900 students) spread across 16 countries, making it the largest educational live security exercise ever performed. We present both the innovative design of this competition and the novel dataset we collected. In addition, we define Cyber Situational Awareness metrics to characterize the toxicity and effectiveness of the attacks performed by the participants with respect to the missions carried out by the targets of the attack.

Towards Detecting Compromised Accounts on Social Networks

Compromising social network accounts has become a profitable course of action for cybercriminals. By hijacking control of a popular media or business account, attackers can distribute their malicious messages or disseminate fake information to a large user base. The impacts of these incidents range from a tarnished reputation to multi-billion dollar monetary losses on financial markets. In our previous work, we demonstrated how we can detect large-scale compromises (i.e., so-called campaigns) of regular online social network users. In this work, we show how we can use similar techniques to identify compromises of individual high-profile accounts. High-profile accounts frequently have one characteristic that makes this detection reliable—they show consistent behavior over time. We show that our system, were it deployed, would have been able to detect and prevent three real-world attacks against popular companies and news agencies. Furthermore, our system, in contrast to popular media, would not have fallen for a staged compromise instigated by a US restaurant chain for publicity reasons.

CAPTCHA smuggling: hijacking web browsing sessions to create CAPTCHA farms

CAPTCHAs protect online resources and services from automated access. From an attackers point of view, they are typically perceived as an annoyance that prevents the mass creation of accounts or the automated posting of messages. Hence, miscreants strive to effectively bypass these protection mechanisms, using techniques such as optical character recognition or machine learning. However, as CAPTCHA systems evolve, they become more resilient against automated analysis approaches. In this paper, we introduce and evaluate an attack that we denote as CAPTCHA smuggling. To perform CAPTCHA smuggling, the attacker slips CAPTCHA challenges into the web browsing sessions of unsuspecting victims, misusing their ability to solve these challenges. A key point of our attack is that the CAPTCHAs are surreptitiously injected into interactions with benign web applications (such as web mail or social networking sites). As a result, they are perceived as a normal part of the application and raise no suspicion. Our evaluation, based on realistic user experiments, shows that CAPTCHA smuggling attacks are feasible in practice.